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The Rubik cube is 3D puzzle with 6 different colored faces. The classic puzzle is a 3x3x3 cube with 43 quintillion possible permutations having a complexity of NP-Hard. In this paper, new metaheuristic approaches based on Simulated Annealing (SA) and Genetic Algorithm (GA) proposed for solving the cube. The proposed algorithms are simulated in Matlab software and tested for 100 random test cases. The simulation results show that the GA approach is more effective in finding shorter sequence of movements than SA, but the convergence speed and computation time of the SA method is considerably less than GA. Besides, the simulation of GA confirms the claim that the cube can be solved with maximum 22 numbers of movements.

Rubik Cube, Simulated Annealing, Genetic Algorithm

[1]    El-Sourani,N., Hauke, S., Borschbach, M. An evolutionary approach for solving the Rubik’s cube incorporating exact methods. EvoApplications, Part I, LNCS 6024, 80-89, 2010.

[2]    Korf,R.E. Finding optimal solutions for Rubik’s cube using pattern databases, American Association for Artificial Intelligence. (www.aaai.org), 1997.

[3]    Lee,D., Miller, W. A prolog program and demonstration of an efficient heuristic search method, Ziff-Davis magazine, 2(4), 1997.

[4]    Kapadia,M., Deshpande, M.V., Umale, J. Rubik’s heuristic, Mumbai, India, Electro Info-Com, 2007.

[5]    Kirkpatrick,S., Gelatt, C.D., Vecchi, M.P. Optimization by Simulated Annealing, Science 220, 1983.

[6]    Aarts,E.H.L., Korst, H.H.M. Simulated Annealing and Boltzmann Machines, Wiley,Chichester, 1989.

[7]    El-Sourani,N., Hauke, S., Borschbach, M. Design and Comparison of two Evolutionary Approaches for Solving the Rubik’s Cube, Parallel Problem Solving from Nature,PPSN XI, 442-451, 2010.

[8]    El-Sourani,N. Design and Benchmark of different Evolutionary Approaches to Solve the Rubik`s Cube as a Discrete Optimization Problem. Diploma Thesis, WWU Muenster, Germany, 2009.

[9]    Anil, K. S., Solution for Rubik’s Cube by Using Genetic Algorithm, International Journal of Engineering Sciences & Research Technology, 636-641, 2015.

[10]   Mantere, T., Solving Rubik`s cube with cultural algorithm, unpublished, 2012.

[11]   Smith, R. J., Kelly, S., Heywood, M. I., Discovering Rubik`s Cube Subgroups using Co-evolutionary GP: A Five Twist Experiment, GECCO `16 Proceedings of the Genetic and Evolutionary Computation Conference, 789-796, 2016.

[12]  Hewa Majeed Zangana "A New Algorithm for Shape Detection",IOSR-JCE, Vol. 19, Issue 3, pp. 71-76, 2017.

[13]  K. Nandhini, B. Gomathi, "Implementation of LSB Based Steganography Algorithms in FPGA", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.5, pp.32-37, 2018

[14]  Riddhi H.Shaparia, Narendra M.Patel, Zankhana H. Shah, "Flower Classification using Different Color Channel",International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.2, pp.1-6, 2019

Hewa Majeed Zangana, "Using Genetic Algorithm With Simulated Annealing To Solve Rubik Cube," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.1-6, 2020.

This paper talks about the prediction of types of landslides. It employs Random Forest Classifier technique, the ensemble version of Decision Trees. The results of the experiment show that ensemble techniques provide a better result compared to other algorithms. The dataset used here, in this paper, is Landslides After Rainfall dataset from NASA. This model achieves 59% accuracy without feature selection and 84% accuracy with feature selection.

Artificial Intelligence, Machine Learning, Decision Tree, Ensemble Learning, Random Forest Classifier

[1]    
Gwo-Fong Lina,
Ming-JuiChanga, Ya-ChiaoHuanga, b and Jui-Yi Hob, “Assessment of susceptibility to rainfall-induced
landslides using improved self-organizing linear output map, support vector
machine, and logistic regression”, Elsevier., vol. 224, pp. 62-74, May 2017.

[2]     J.N. Goetz, A. Brenning, H. Petschko and
P.Leopold, “Evaluating machine learning and statistical prediction
techniques for landslide susceptibility modeling”, Elsevier., vol. 81, pp. 1-11, August 2015.

[3]    
M.T.
Brunettia, M. Melilloa, S. Peruccaccia, L. Ciabattaa, b, L. Broccaa, “How
far are we from the use of satellite rainfall products in landslide
forecasting?”, Elsevier., vol. 210, pp. 65-75, June 2108.

[4]    
MilošMarjanović, MilošKovačević, BranislavBajat, VítVoženílek, “Landslide susceptibility assessment
using SVM machine learning algorithm”, Elsevier.,
vol. 123, pp. 225-234, November 2011.

[5]     Milošmarjanovic;branislavbajat; miloškovacevic,
“Landslide Susceptibility Assessment with Machine Learning Algorithms”,
International Conference on Intelligent Networking and Collaborative Systems,
pp. 273-278, December 2009.

[6]     Cheng Lian, Zhigang Zeng, Wei Yao and Huiming Tang, “Performance of Combined
Artificial Neural Networks for Forecasting Landslide Displacement”, International
Joint Conference on Neural Networks (IJCNN), pp. 418-423, July 2014.

[7]    
Cheng Lian, C.
L. Philip Chen, Zhigang Zeng, Wei Yao, and Huiming Tang, “Prediction Intervals for Landslide Displacement Based
on Switched Neural Networks”, IEEE
Transactions on Reliability., vol. 65,
pp. 1483-1495,
September 2016.

[8]    
Yu
Huang, Lu Zhao, “Review on landslide susceptibility mapping using support
vector machines”, Elsevier., vol. 165, pp. 520-529, June 2018.

[9]     MunirahRadinMohd Mokhtar, Abdul Nasir Matori and
Khamaruzaman,
“Observing Landslide Occurrence by GIS Approach”, IEEE 4th Control and System Graduate Research Colloquium., pp. 21-26, August 2013.

[10]  Gege Jiang, Yuan
Tian and Chenchao Xiao, “GIS-based Rainfall-Triggered Landslide Warning and
Forecasting Model of Shenzhen”, 21st International Conference on
Geoinformatics, October 2013.

Harish Kumar N.G., Pooventhiran G., Karthika Renuka D., "Landslide Type Prediction using Random Forest Classifier," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.7-11, 2020.

Data mining, Big data cluster analysis, Fuzzy, Linkage

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Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery
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Klawnn, F.; Kruse, R.; Runkler, T.;. Fuzzy Cluster Analysis: “Methods for
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“Comparing nearest neighbor algorithms in highdimensional space,” 2011.

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J. C. Bezdek, “An efficient formulation of the improved visual assessment of
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[5]    
D. Kumar, M.
Palaniswami, S. Rajasegarar, C. Leckie, J. C. Bezdek, and T. C. Havens,
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Alshatri, Z. Tari, A. Alamri, I. Khalil, A. Y. Zomaya, S. Foufou, and A.
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Keller, J. Bezdek, and A. Zare, “Random projections fuzzy c-means (RPFCM) for
big data clustering,” in Proc. IEEE Int. Conf. Fuzzy Syst., pp. 1–6, 2015.

[8]    
D. Kumar, J. C.
Bezdek, M. Palaniswami, S. Rajasegarar, C. Leckie, and T. C. Havens, “A hybrid
approach to clustering in big data,” IEEE Trans. Cybern., vol. 46, no. 10, pp.
2372–2385, Oct. 2016.

[9]    
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and M. Palaniswami, “Ensemble fuzzy clustering using cumulative aggregation on
random projections,” IEEE Trans. Fuzzy Syst., vol. 26, no. 3, pp.
1510–1524, Jun. 2018.

[11]  P. Rathore, A. S. Rao, S. Rajasegarar, E. Vanz, J.
Gubbi, and M. Palaniswami, “Real-time urban microclimate analysis using
internet of things,” IEEE Internet Things J., vol. 5, no. 2, pp.
500–511, Apr. 2018.

[12]  Punit Rathore, Dheeraj Kumar, James C. Bezdek,
Sutharshan Rajasegarar, and Marimuthu Palaniswami, "A Rapid Hybrid
Clustering Algorithm for Large Volumes of High Dimensional Data", IEEE
TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 31, NO. 4, APRIL 2019.

[13]  J. C. Dunn, “A fuzzy relative of the ISODATA process
and its use in detecting compact well-separated clusters,” J. Cybern., vol. 3, no. 3, pp. 32–57, 1973.

[14]  P. Rathore, Z. Ghafoori, J. C. Bezdek, M. Palaniswami,
and C. Leckie, “Approximating Dunn’s cluster validity indices for partitions of
big data,” IEEE Trans. Cybern.

R. Kiruthika, V. Vijayakumar, "An Enhanced Fuzzy Based Linkage Clustering Algorithm (EFCA) in High Dimensional Data," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.12-17, 2020.

Due to the introduction of FACT devices in power supply system and complicated control in power industry, presence of harmonics in power supply system is a common problem. It increases day by day and it distorts and unbalances the supply voltage. Due to unequal transformer tap settings, open delta connected transformer banks, unbalanced distribution of single phase loads supply voltage unbalance may occurs. Three phase induction motor is the main workhouse of modern industries. Due to the application of such a polluted supply voltage the performance of three phase induction motor may be affected seriously and this may affect the load connected with the drive system. To take correct measure during torque and speed control, it is required to know the performance during voltage unbalance and distorted supply due to different harmonics. To mitigate this, an attempt has been made to develop a generalized model of a three phase induction motor for online performance study of torque and speed during different distorted and unbalance supply voltage. In doing this, Matlab/Simulink has been used to develop a model based on generalized theory of induction motor. Finally, using the model, performance has been studied during application of different distorted and unbalanced supply and reported.

Modelling of Induction Motor, Torque and speed monitoring, Harmonics and unbalance voltage detection

[1]    
C. Y. Lee,W. J.
Lee,Y. N. Wang,J. C. Gu,“Effects of Voltage Harmonics on the Electrical and
Mechanical Performance of a Three-phase Induction Motor”, IEEE Industrial
and Commercial Power Systems Technical Conference, pp.88-94,  May 1998.

[2]    
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Scheduling in Grid Environment”, International Journal of Scientific
Research in Computer Science and Engineering, E-ISSN: 2320-7639, Vol. 5, Issue 6, pp. 79-82, December 2017.

[3]    
S. Joshi and D.K.
Rai, “Design and Simulations of Load
Management Impact on Power System”, International Journal of Scientific
Research in Computer Science and Engineering, E-ISSN: 2320-7639, Vol.6, Issue 2, pp 20-26, April 2018.

[4]    
K.M. Rudrappa and
B.P. Divakar, “A Programmable System on
Chip (PSOC) for Active Power Filter (APF) Based on Cortex M3“,
International Journal of Scientific Research in Network Security and
Communication, ISSN: 2321-3256, Vol. 5,
Issue 2, pp.37-43, May 2017.

[5]    
T. Jackie and C.
Hung, “Neural network Method: Loss
Minimization control of a PMSM with core Resistance Assessment“,International
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2321-3256, Vol. 4, Issue 6, pp. 10-18, December 2016.

[6]    
G Maruthi, K. P.
Vittal,“Electrical Fault Detection in
Three Phase Squirrel Cage Induction Motor by Vibration Analysis using MEMS
Accelerometer”,International Conference on Power Electronics and Drives
System,pp. 843-848, 28 November-1 December, 2005.

[7]    
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Voltages Serving an Induction Motor”, Rural Electric Power conference, pp. 1-8, May 2000.

[8]    
L. Xu, Y. Wang, “Dynamic modeling and control of DFIG-Based wind turbines under
unbalanced network conditions”, IEEE Transactions
on Power Systems, Vol.22, Issue 1, pp. 314 – 323, 2007.

[9]    
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Control of a doubly fed Induction wind
generator under unbalanced grid voltage conditions”, IEEE Transactionson Energy Conversion, Vol.22, No. 1,pp. 129 – 135, 2007.

[10]  A. Ghosh, A. Das, A. Sanyal, “Effect of Saturation in Induction
Machines” International Journal of
Analysis of Electrical Machines, Vol. 4,
No. 1, pp. 25-29, 2018.

[11]  A. Ghosh, A. Das, A. Sanyal, “Equivalent Circuit Parameters of a
Synchronous Machine from Manufacturer`s Data”, Annals of the Faculty of Engineering Hunedoara –
International Journal of Engineering, Vol. 16,
No. 3, pp. 127-131, 2018.

[12]  A. Ghosh, A. 
Das, A. Sanyal, “Transient Stability Assessment of an
Alternator Connected to Infinite Bus Through a Series Impedance Using State
Space Model”, Journal of The
Institution of Engineers (India): Series B, Vol. 100, No.5, pp. 509–513, October 2019.

[13] 
L. El Menzhi, A.
Saad,“Induction motor fault diagnosis
using voltage Park components of an auxiliary winding - voltage unbalance”,International Conference on Electrical
Machines and Systems,pp. 1–6, May 2009.

[14] 
A. K. Thakur, P. K.
Kundu, A. Das, “Comparative study of
induction motor faultanalysis using feature extraction”, IEEE Calcutta
Conference, pp. 150-154, December,2007.

[15] 
J. M. Corres, J.
Bravo, F. J. Arregui, I. R. Matias, “Unbalance
and Harmonics Detection in Induction Motors Using an Optical Fiber Sensor”,IEEE
Sensors Journal, Vol.6, Issue3, pp. 605-612, 2006.

[16] 
D. R. Sawitri, D.
A. Asfani, M. H. Purnomo, I. K. E. Purnama, M. Ashari, “Early Detection of Unbalance Voltage in Three Phase Induction Motor
Based on SVM”, 9^th IEEE International Symposium on
(SDEMPED), pp. 573-578, August 2013.

[17] 
A. K. Thakur, P.
K. Kundu, “Authentication of unknown
fault for a three phase induction motor by stator current spectral analysis”,
2^nd International Conference on Control,Instrumentation, Energy
& Communication(CIEC), pp. 279-283,
January 2016.

[18] 
A.R.Sedighi,“A new model for high impedence fault in
electrical distribution systems”, International Journal of Scientific
Research in Computer Science and Engineering, Vol. 2, Issue 4, pp. 6-12, August2014.

[19] 
M.L. Sin, W.L. Soong and N.
Ertugrul,“Induction machine on-line condition monitoring and fault
diagnosis - a survey”, AUPEC2003:Australasian Universities Power Engineering Conference,
Christchurch, New Zealand. pp. 1-6, 2003.

[20] 
F.C. Trutt, J. Sottile, J. L. Kohler,“
Online Condition Monitoring of Induction Motor”,IEEE Transactions on
Industry Apllications, Vol. 38, No. 6,  pp. 1627-1632,November/December2002.

[21] 
Sang Bin Lee, Jinkyu Yang, Karim Younsi, Raj Mohan Bharadwaj, “An Online Groundwall and phase to phase
Insulation Quality Assesment Technique for AC Machine Stator Windings”,IEEE Transaction on Industry Apllications,Vol. 42, No. 4, pp. 945-957,2006.

[22] 
O. Mustapha, M.
Khalil, G. Hoblos, H. Chafouk, H. Chafouk, D. Lefebvre, “On-Line Fault Detection by Using Filters Bank and Artifi`cial Neural
Networks”, Information and Communication Technologies, ICTTA `06. 2^nd,
Volume: 1, pp : 1630-1634, 2006.

[23] 
A. Bethge, Patrick K.-W. Lo, J. T. Phillipson, “On-Line Monitoring of Partial Discharges on Stator Windings of Large
Rotating Machines in the Petrochemical Environment”,IEEE Transactions on
Industry Applications, Vol. 34, No. 6, pp. 1359-1365, 1998.

[24] 
R. Ong, J. H.
Dymond, R. D. Findlay, B. Szabados, “Shaft
Current in AC Induction Machine—An Online Monitoring System and Prediction
Rules”,IEEE
Transactions on Industry Applications, Vol. 37, No. 4, pp. 2822-2829,June/August 2001.

[25] 
J. Policarpo, G.
de Abreu, J. S. de Sa, Claudio C. hado, “Harmonic
torque in three phase induction motors supplied by non sinusoidal voltages”,11thInternational Conference on
Harmonics and Quality of Power,pp. 652-657,
2004.

[26] 
S. Ghanbari, P.
Sheikhzadeh-Baboli ,R. F. Naghib, “Three-Phase
Induction Motor’s Torque under Voltage Unbalance”,Global Journal of
Researches in Engineering Electrical and Electronics Engineering, Vol. 13, Issue 9, Version 1.0, pp: 29-33, 2013.

[27] 
P. Pillay, M. Manyage, “Definitions of Voltage
Unbalance”, IEEE Power Engineering
Review, Vol. 22, No. 11, pp. 49–50, 2002.

[28] 
S. Shah, A. Rashid, MKL Bhatti, “Direct Quadrate (D-Q)
Modeling of 3-Phase Induction Motor Using MatLab / Simulink”, Canadian Journal on Electrical and Electronics
Engineering Vol. 3, No. 5, pp. 237-243, May 2012.

[29] 
K. L. Shi, T . F.
Chan, Y. K. Wong, S. L . Ho, “Modelling
and Simulation of the three-phase Induction Motor Using Simulink”,
International Journal of Electrical Engineering Education, Vol. 36, pp. 163-172, Manchester U.P, 1999.

Arunava Kabiraj Thakur, Arabinda Das, Palash Kumar Kundu, "Development of Generalized Model of 3-Phase Induction Motor for Performance Study During Different Distorted and Unbalance Voltage," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.18-25, 2020.

Intravascular Ultrasound (IVUS) is a surgical representational process which used to see the plasma vessels out through the conterminous blood column by blood vessels in persons to determine the amount of accretion of degenerative substantial built up at in the pericardial coronary vein which cannot be envisaged by Angiography. It harvests the vessel fractious sectional images of plasma vessels that provide the measureable and qualitative valuation of the vascular wall info about the nature of atherosclerosis abrasions as well as plaque size and shape. The credentials of lumen, media and adventitia restrictions in IVUS imaginings is essential for an effectual assessment of the atherosclerotic commemorations. During an IVUS inspection, a catheter with an ultrasound transducer is announced in the physique through a plasma container and then dragged back to appearance sequence of container cross sections. This paper accessible a one of the good-looking and collaborating methods is the Active Curve Prototypical Method (ACM) with Limited Image Fit Dynamism Minimization (LIFEM) method which has been widely used in medical imaging performance as it always produces computationally well-organized for sub-regions with incessant boundaries. In our approach preserves and deals with the boundary regularization property and sub-pixel exactitude.

Intravascular ultrasound (IVUS), Vessel Fractious Sectional Images, Credentials of Lumen, Active Curve Prototypical Method (ACM) with Limited Image Fit Dynamism Minimization (LIFEM), Boundary Regularization

[1]      Alison Noble,
Djamal Boukerroui. “Ultrasound image
segmentation a survey”, IEEE Transactions on Medical Imaging, Institute of
Electrical and Electronics Engineers, 25(28),
pp.987-1010, 2006.

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Processing, 10(2), pp.266-277, 2001.

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Xiangmin Zhang, M.Seibes, M.S.Bissing, S.C.DeJong, S.M.Collins, C.R.McKay, “Segmentation of intravascular ultrasound
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[5]    
J.D.Klingensmith, R.Shekhar, D.G.Vince, “Evaluation of three-dimensional
segmentation algorithms for the identification of luminaland
medical-adventitial borders in intravascular ultrasound images”, IEEE
Transactions on Medical Imaging, 19(10),
pp.996-1011, 2000.

[6]    
George D.Giannoglou, Yiannis Chatzizisis, Vassilis
Koutkias, Ioannis Kompatsiaris, Maria Papadogiorgaki, Vasileios Mezaris, Eirini
Parissi,  Panagiotis  Diamantopoulos, Michael G.Strintzis NicosMaglaveras,
George E.Parcharidis, George E.Louridas, “A
novel active contour model for fully automated segmentation of intravascular
ultrasound images in vivo validation in human coronary arteries”,   Computers in Biology and Medicine, 37(9),pp.1292-1302. 2007.

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on  Image processing, 7(3), pp.359-369, 1998.

[8]     HassenLazrag, KamelAloui, Med Saber Naceur, “Automatic Segmentation of Lumen in
Intravascular Ultrasound Images Using Fuzzy Clustering and Active Contours”,
International Conference on Control, engineering & Information Technology
(CEIT’13) Proceedings Engineering & Technology, Vol.1, pp.58-63, 2013.

[9]     Elisabeth Brusseau, Chris L.de Korte, Frits Mastik,
Johannes Schaar, and Anton F.W.Van der Steen, “Fully Automatic Luminal Contour Segmentation in Intracoronary
Ultrasound Imaging  A Statistical
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active contour segmentation”, IEEE Transactions on Pattern Analysis and
Machine Intelligence, 26(6), pp.799-803, 2004.

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“Endocardial border Identification in
two-dimensional echocardiographic images, Review of Methods”,Computerized
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[13]  E. Brusseau, C.L. de Korte, F. Mastik, J. Schaar, A.F.
van der Steen, “Fully automatic luminal
contour segmentation in intracoronary ultrasound imaging”-A statistical
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554–566, 2004.

[14]  J.D. Klingensmith, R. Shekhar, D.G. Vince, “Evaluation of threedimensional segmentation
algorithms for the identification of luminal and medial-adventitial borders in
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[15]  G.Sowmiya, V.Kumutha, “Facial Expression Recognition
Using Static Facial Images ”, International journal of Scientific Research in
Computer Science and Engineering , Vol.6, Issue.2, pp.72-75,2018.

C. Priyanka, M. Vanitha, S. Anitha, "Automatic Segmentation of Lumen in Intravascular Ultrasound Images Using Limited Image Fit Dynamism Minimization (LIFEM) Technique," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.26-30, 2020.

Cardio vascular disease is the most prominent cause of death worldwide. Machine Learning Algorithms can be used for predicting chances of heart disease occurrence.  Relating machine learning and data mining methods is a strategic approach to consume large volumes of available Cardio-related data for prediction. The datasets used are classified in terms of medical parameters. In this paper, numerous algorithms and techniques are discussed that are used in prediction of Cardio Vascular Diseases. Fast Correlation-Based Feature Selection (FCBF) method to filter noise data to improve quality of heart disease classification. K-Nearest Neighbour, Support Vector Machine, Naïve Bayes, Random Forest and a Multilayer Perception, Artificial Neural Network optimized by Particle Swarm Optimization (PSO) combined with Ant Colony Optimization (ACO) are the classification algorithms used. By using machine learning algorithms and deep learning it provides numerous ways for the prediction of the heart disease. There are various methods which provide us an information and these are applied to various datasets to get particular results. 

Heart Disease, Predictive Analysis, Naïve Bayes, Decision Tree, SVM

[1]. Youness
Khourdifi Mohamed Bahaj, ”Heart Disease Prediction and Classification Using
Machine Learning Algorithms Optimized by Particle Swarm Optimization and Ant
Colony Optimization”, International Journal Of  Intelligent Engineering and System, October-2018.

[2].
S.Kavitha, K.R.Baskaran,S.Sathyavathi, ”Heart Disease with Risk Prediction
using Machine Learning Algorithms”, International Journal of Recent Technology
and Engineering (IJRTE), ISSN: 2277-3878,
Volume:7 ,Issue:4S, pp. 314-317,  November
2018.

[3].
SenthilKumar Mohan, Chandrasegar Thirumalai, and Gautam Srivastava, “Effective
Heart Disease Prediction Using Hybrid Machine Learning Techniques”, IEEE Access,
ISSN:2923-707, Volume:10, July 3 2019.

[4]. Niraj
Kalantri, Kumar R, “Predictive Analysis on Heart Disease Using Different
Machine Learning Techniques”, Internation Journal of Computer Science And
Engineering, ISSN:97-101, Volume:7, Issue:2,  28-Feb-2019.

[5]. Amin Ul
Haq , Jian Ping Li ,Muhammad Hammad Memon ,Shah Nazir ,and Ruinan Sun, "A
Hybrid Intelligent System Framework for the Prediction of Heart Disease Using
Machine Learning Algorithms", Hindawi Mobile Information Systems, ISSN:3860-146, Volume: 2018, pp. 1-21

[6]. Himanshu
Sharma,M A Rizvi,"Prediction of Heart Disease using Machine Learning
Algorithms", International Journal On Recent And Innovative Trends In
Computing And Communication, ISSN: 2321-8169,
Volume:5, Issue:8, pp. 99-104, August-2017

[7]. V.V.
Ramalingam, Ayantan Dandapath, M Karthik Raja, "Heart disease prediction
using machine learning techniques", International Journal Of Engineering
And Technology, Volume:7, Issue:2.5, pp. 684-687, March 2018

[8]. Apurva
Gaikwad, M.S. Panse, “Extraction
of FECG from Non-Invasive AECG signal for Fetal Heart Rate Calculation”, ISSN:2321-3256, Volume:5, Issue:3, pp. 1069-112, 2017

[9]. Alister Dsouza, M. S. Panse, “LabVIEW based
detection of Pulse Transit Time from Plethysmogram and ECG signals for
estimation of Blood Pressure”, ISSN: 2320-7639,
Volume:5, Issue:4, pp. 36-40, 2017

Bhumika J., Rashmi R. Kotiyan, Sonal T.H., Lakshmi R., "A Survey on Early Detection and Prediction of Heart Diseases using Machine Learning and Data Mining Techniques," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.31-34, 2020.

The project proposed here is an automatic public garden system which uses a PIC16F877A controller. The PIC controller is used to control the entire system. The hardware components required for this project are PIC16F877A, timer, battery, DC motor, humidity sensor, relay, solar panel, water level sensor, voice module, IR sensor. The project is an automatic design in which PIC controller controls the entire public garden system like gate, water system, lights and dustbin. Initially, the controller switches ON the entry gate that is opened for certain time, after some time the exit gate will also open. A voice indication is given to alert the public for closing time of garden then both gates will be closed. An IR sensor is fixed in the exit gate which is used to exit the people stuck inside the garden after closing time. Lights are automatically turned ON and OFF using timer. Based on humidity sensor the DC motor will supply water by using water sprinklers. If the water level in tank is beyond a certain fixed level the motor will automatically ON and fills the tank by using water level sensor. For disposal of garbage we introduce a smart garbage system which has two dustbins in which if one dustbin is filled by trash it gets automatically closed and another dustbin will be opened. The filled dustbin is indicated by LED and a message is send to the corresponding authorities for disposal. The overall power supply to the garden is generated and provided internally using a renewable energy.

PIC16F877A, Timer, Moisture sensor, Water level sensor, IR sensor, GSM, Solar panel

[1] Pawar P M , Pawar S D,
Vare J A, “Public garden automation”,
Journal of Information, Knowledge and Research in Electronic and Communication,
Vol. 4, Issue 02, pp:1446-1449, (2016.

 [2] Mr. Mahadev Vilas, Mr. MahanavarSagar, Mr.
Ingale Gaurav, Prof. Gawali D.S, “Public Garden Automation Using GSM
Technology”, International Journal for Research Trends and Innovation, Vol. 2,
Issue 3, pp:100-102, 2017.

 [3] S.Abirami, M.Hemalatha, “IOT Based Indoor garden Monitoring System”,
International Journal of Contemporary Research in Computer Science and Technology,
Vol. 3, Issue 3, pp: 27-29, (2017).

[4] Norakmarbinti Arbain
sulaiman, Muhamad Dan Darrawi bin Sadli, “An
IoT-based Smart Garden with Weather Station system” IEEE paper 2019,
pp: 38-43, 2018.

[5] Ramkumar E, Nagarani S,
Roger Rozario A P, Arjunman Banu S, “Tomen:
A Plant Monitoring and Smart Gardening System uning IoT”, International
Journal of Pure and Applied Mathematics, Vol.199, Special issue, pp:703-709,  2018.

 [6] R.B.Thombare, Sawakhande Pranita, Dinde
Swati, Patil Swati, “Smart Public Garden
by Using Arduino”, International
Conference on recent Innovation in Science, Engineering and Management, pp:679-683,
2018.

[7]  Mallikarjunaiah H B, Prasanna Kumar, “Soil Characteristic Analysis Using IOT RM”,
International journal of computer science and engineering, Vol.7,
Special issue 15, 2019.

[8] Soham Datta, Amrita saha,
Debabrata paul,  Roni jana, Amartya roy, “Realtime Monitoring of Soil Moisture for  Automatic plant Watering System”,
International journal of computer science and engineering, Vol.7,Special
issue 8, 2019.

N. Vani, M. Varatharaj, P. Jayanthi, P.S. Vishal kumar, S. Maheswari, R. Nishanthakumar, "Opulent Futuristic Smart Sensing Garden," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.35-38, 2020.

In present study our attempt is to generate electric power from plant and soil. We fabricated microbiological soil cell by using different plantand soil samples (Laterite, Red Alluvial and saline soil). Certain amount of soil sample diluted with water which act as electrolyte and Copper and Zinc plate use as positive and negative electrode respectively. Maximum value of 0.9 Volt has been obtain from cell having saline soil. Water hyacinth and gametophyte plant grown in the soilto construct plant microbiological soil cell.The plant soil cell so constructed are found to providestable voltage of about 1.5 volt per cellfor time of about one month. The cell so constructed is pollution free, eco-friendly and found to be rechargeable by adding specific quantity of water. The working model based on this research is also selected for National Level Science exhibition.

Soil cell, OCV, Microbiological fuel cell

[1] Bombelli P,
Dennis RJ, Felder F, Cooper MB, Iyer DMR, Royles J, et al. Electricaloutput of
bryophyte microbial fuel cell systems is sufficient to power a radio or an environmental
sensor. R Soc Open Sci. 3:160249, 2016.

[2] Helder M, Chen
WS, Harst EJ, Strik DP, Hamelers HBV, Buisman CJ, et al. Electricity production
with living plants on a green roof: environmental performance of the plant‐microbial fuel cell. Biofuels BioprodBioref
 7:52–64, 2013.

[3] C.J.N. Buisman,
H.V.M. Hamelers, D.P. Strik, Electricity generation with living Plants from lab
to application, Ph.D Thesis, Wageningen University, 2012.

[4] D.P. Strik, H.
V. M. Hamelers, F.H.J. Snel, C.J.N. Buisman, Green electricity production with
living plants and bacteria in a fuel cell, Wiley Inter Science, , 32, 870–876, 2008.

[5] Timmers RA,
Strik DP, Hamelers HV, Buisman CJ. Long-term performance of a plant microbial
fuel cell with Spartinaanglica. ApplMicrobiolBiotechnol;86:973–81, 2010.

[6] Bacilio-Jiménez
M, Aguilar-Flores S, Ventura-Zapata E, Pérez-Campos E, Bouquelet S, Zenteno E.
Chemical characterization of root exudates from rice (Oryzasativa) and their
effects on the chemotactic response of endophytic bacteria. Plant Soil
249:271–7, 2003.

[7] Chiranjeevi P,
Mohanakrishna G, Mohan SV. Rhizosphere mediated electrogenesis with the
function of anode placement for harnessing bioenergy through CO2 sequestration.
BioresourTechnol 124:364–70, 2012.

[8] Takanezawa K,
Nishio K, Kato S, Hashimoto K, Watanabe K. Factors affecting electric output
from rice-paddy microbe al fuel cells. BiosciBiotechnolBiochem 74:1271–3, 2010.

[9] Kaku N, Yonezawa
N, Kodama Y, Watanabe K. Plant/microbe cooperation for electricity generation
in a rice paddy field. ApplMicrobiolBiotechnol 43–9, 2008.

[10] D.P. Strik,
H. V. M. Hamelers, F.H.J. Snel, C.J.N. Buisman, Green electricity production
with living plants and bacteria in a fuel cell, Wiley Inter Science, , 32,
870–876, 2008.

[11] V. Smil, A.J.
Leggett, W.D. Philips, C.L. Harper, Visions of discovery: New light on physics,
cosmology and consciousness, Cambridge University Press, 2010.

[12] M. Gezginci, Y.
Uysal, Electricity generation using different substrates and their different
concentrations in microbial fuel cell, Journal of Environmental Protection and
Ecology, 15, 1744–1750, 2014.

[13] M Helder, D.P
Strik, H.V.M. Hamelers, C.J.N. Buisman, The flat-plate plant-microbial fuel
cell: the effect of a new design on internal resistances, Biotechnology for
Biofuels, 5, 70. 2012.

Nirbhay K Singh, Monika Singh, "Fabrication and Characterization of a Novel Eco-Friendly Plant Microbial Soil Cell," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.39-41, 2020.

The main intent of enhancing the present system of object detection is to enhance its productivity and improve the detection accuracy. The method initializes a model in a way to store the background pixels and to compare each frame pixels with the same model background pixels. The intention is to separate the background pixels with the foreground moving pixels to improve the ability to detect the object in the foreground. An improved object detection algorithm based on “Gaussian mixture model” and “three-frame difference method”. This three-frame difference method uses a dynamic segmentation threshold and edge detection technology to solve the problem of illumination mutation and discontinuity of target edge.

Gaussian mixture model, illumination mutation, object detection, pixels, three-frame difference method, dynamic segmentation, edge detection technology

[1]
Chen, Liang-Bi & Su, Jian-Ping & Chen, Ming-Che & Chang, Wan-Jung
& Yang, Ching-Hsiang & Sie, Cheng-You, 
“An Implementation of an Intelligent Assistance System for Visually
Impaired/Blind People”, IEEE Access, 12-Apr-2019.

[2]
Mahajan J.R., C.S. Rawat, “Object Detection and Tracking using Cognitive
Approach”, International Journal of Scientific Research in Network Security and
Communication, ISSN: 2321-3256, Volume:5, Issue.3, pp.136-140,
Jun-217

[3] A.
N. Zereen and S. Corraya, "Detecting real time object along with the moving
direction for visually impaired people", International Conference on
Electrical, Computer & Telecommunication Engineering (ICECTE),  pp.1-4, Dec-2016.

[4] A.
Jinguji, Y. Sada and H. Nakahara, "Real-Time Multi-Pedestrian Detection in
Surveillance Camera using FPGA",   International Conference on Field
Programmable Logic and Applications (FPL), Barcelona, Spain, pp.424-425,
2019.

[5] I.
Akbar and A. F. Misman, "Research on Semantics Used in GPS Based Mobile
Phone Applications for Blind Pedestrian Navigation in an Outdoor
Environment",  International
Conference on Information and Communication Technology for the Muslim World
(ICT4M), pp. 196-2018.

[6] M.
Awad, J. E. Haddad, E. Khneisser, T. Mahmoud, E. Yaacoub and M. Malli,
"Intelligent eye: A mobile application for assisting blind people", IEEE
Middle East and North Africa Communications Conference (MENACOMM), pp.1-6,
2018.

[7] S.
Sharma, S. Jain and Khushboo,  and Kulyukin,
Vladimir & Nicholson, John & Ross, David & Marston, James &
Gaunet, Florence, “The Blind Leading the Blind: Toward Collaborative Online
Route Information Management by Individuals with Visual Impairments”, .
International conference on Social Information Processing, pp.54-59, March
26-28, 2008.

[9] Yu,
Tianming & Yang, Jianhua & Lu, Wei, “Combining Background Subtraction
and Convolutional Neural Network for Anomaly Detection in Pumping-Unit
Surveillance”.

[10]
Baygin, Mehmet & Karakose, Mehmet & Sarimaden, Alisan & Akin, Erhan,
“An Image Processing based Object    Counting
Approach for Machine Vision Application”, International Conference on Advances
and Innovations in Engineering (ICAIE),16-Feb-2018.

[11] S.
L. Joseph, X. Zhang, I. Dryanovski, J. Xiao, C. Yi and Y. Tian, "Semantic
Indoor Navigation with a Blind-User Oriented Augmented Reality", IEEE
International Conference on Systems, Man, and Cybernetics, Manchester,pp.3585-3591,
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[12] C.
K. Lakde and P. S. Prasad, "Navigation system for visually impaired
people", International Conference on Computation of Power, Energy,
Information and Communication (ICCPEIC), pp.0093-0098, 2015 .

[13] Md.T. . Akhtar, S.T. Razi, K.N. Jaman, A.
Azimusshan, Md.A. Sohel, “Fast and Real Life Object Detection System Using
Simple Webcam”, International Journal of Scientific Research in Computer
Sciences and Engineering, ISSN: 2320-7639, Volume:6, Issue.4, pp.18-23,
Aug-2018.

N. Raviteja, M. Lavanya, S. Sangeetha, "An Overview on Object Detection and Recognition," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.42-45, 2020.

Government organizations, citizens, businesses are being victimized by cyber-attacks, crimes, and threats. Globally people have been using cyber safety in every sector of daily life but huge numbers of criminal activities are increasing day by day using ICT tools and applications. The purpose of this paper is to explorer the high risk of cyberattacks, crime, threat and future challenges in Nepal. The study was carried out using content analysis, analysis of various survey reports and in-depth interviews with subject experts. The study claims that high risk of a cyberattack, crime, and threat have been increasing unexpectedly in various sector of Nepalese society. It is essential to make a common strategy to reduce the increasing technical risk related to cyber. The risk of cyberattack, crime, and threat is very high. So, IT audit must be made compulsory in each and every organization and original operating system as well as application software used. Strong cybersecurity precautions and technologies ought to be adopted during the installation of ICT automation. The paper claims that not only the banking sector but also the Government of Nepal should appoint cybersecurity personnel in their services so that the expert team will be able to fight against the cyber attack. As time is demanding ‘Cyber Army’ in Nepal, the Government should not delay forming a new force ‘Cyber Army’.

Cyber crime, Challenges, Cyber threat, Cyber attack, IT audit, e-governance

[1]
  Shailendra Giri. Cyber Crime, Cyber
threat, Cyber Security Strategies and Cyber Law in Nepal. Pramana Research
Journal. Vol. 9, Issue 3, PP. 662-67, 2019.  

[2]   Shailendra Giri and Subarna Shakya. Cybercrime,
Cyber Ethics, Cyber Law and Cyber Security. Proceeding of International
Youth Conference on Science, Technology and Innovation (IYCSTI-2019). Kathmandu,
Nepal, October 21-23,   pp-594-597,
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[3]
  S. Shriram. Cyber Security and
Related Crimes in Indian Senario. International Journal of Current
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pp.5403-5412, 2014

 [4]  Shailendra
Giri and Subarna Shakya. Cloud Computing and Data Security Challenges: A
Nepal Case. International Journal of Computer Trends and Technology. Vol. 67, Issue 3. PP 146-150, 2019.

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SITBON. A Cyber Security Approach for Smart Meters at ERDF. Modeling Cyber
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pp. 18-22, December 2013

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[9]  L. Serena.
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 [12]        Chuda
Bahadur Roka, Cybercrime and Security in Nepal: The Need For Two-Factor
Authentication In Social Media. Crossing the Border: International Journal
of Interdisciplinary Studies, Vol. 5,
Issue 2, pp. 16-31. 15 July 2017.
DOI: https://doi.org/10.3126/ctbijis.v5i2.18436

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[15]
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and Data Security. Journal of Institute of Engineering, Tribhuvan
University, Nepal. Vol. 15,  Issue 2. pp. 226-232 , 2019. 

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Shailendra Giri, Subarna Shakya, "High Risk of Cybercrime, Threat, Attack and Future Challenges in Nepal," International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.46-51, 2020.